How many people develop anti-drug antibodies to the biologic drug tildrakizumab, and what impact does this have on the effectiveness of their treatment
Bibliographic record
Abstract
Biologics are powerful drugs used to treat a range of diseases including psoriasis. They can be very effective; however, the body's immune system, which normally fights off infection, can produce ‘anti‐drug antibodies’ (or ADAs) that see biologics as harmful ‘invaders’ and try to deactivate them. These antibodies may result in serious side effects and/or may reduce the effectiveness of the drug. The authors of this study, based in USA, Canada and Germany, evaluated anti‐drug antibodies in patients participating in three clinical trials of a biologic called tildrakizumab. In the three trials, patients were taking either 100mg or 200mg tildrakizumab. 1400 patients were studied from weeks 12 to 16 of the study, and 780 from weeks 52‐64. Three percent of patients on the 100mg dose developed anti‐drug antibodies. This led to an average reduction in clinical response (meaning the drug did not work so well) at week 52. However, the presence of antibodies was not associated with increased incidence of serious adverse effects (unwanted side effects). Results in patients on the 200mg dose were inconclusive. This drug in the 100mg dose has recently been approved for clinical use in Europe and USA. The authors noted that the incidence of antibodies was less than that observed with other types of biologic drugs. This summary relates to the study: Assessment of the effects of immunogenicity on the pharmacokinetics, efficacy and safety of tildrakizumab
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".